Investigation of residual stresses of multi-layer multi-track components built by directed energy deposition: experimental, numerical, and time-series machine-learning studies

Mirazimzadeh, Seyedeh Elnaz; Mohajernia, Bita; Pazireh, Syamak; Urbanic, Jill; Jianu, Ofelia

doi:10.1007/s00170-023-12661-x

Cited by 2 publications

(1 citation statement)

References 30 publications

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“…It involves using metal powder as the raw material and coaxial powder as a feature, where a high-power laser is used to melt and rapidly solidify the metal powder layer by layer, creating near-net-shape components [2]. However, during the printing process, the presence of a moving heat source leads to a high temperature gradient around the molten pool, resulting in residual stress and deformation in the manufactured components [3,4]. These issues can negatively impact the accuracy and quality of the final products [5].…”

Section: Introductionmentioning

confidence: 99%

Research on the Deformation Prediction Method for the Laser Deposition Manufacturing of Metal Components Based on Feature Partitioning and the Inherent Strain Method

Li,

Gao,

Yin

et al. 2024

Mathematics

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Laser deposition manufacturing (LDM) has drawn unprecedented attention for its advantages in manufacturing large-scale and complex metal components. During the process of LDM, a large thermal gradient is generated due to thermal cycling and heat accumulation. As a result, large residual stress and deformation are formed in the LDM metal components. Then, the dimensional accuracy of the metal components becomes poor. To achieve deformation control and increase dimensional accuracy, the deformation prediction of metal components is very meaningful and directional. However, the traditional thermoelastic–plastic method can only achieve deformation prediction for small-scale LDM metal components. Because of the low computational efficiency, it is extremely difficult to meet deformation prediction demand for large-scale metal components. Based on feature partitioning and the inherent strain method, a rapid deformation prediction method is proposed for large-scale metal components in this manuscript. Firstly, to solve the problem of poor consistency of formation quality due to the randomness of the partition process, the partitioning process was established according to typical geometric features. Secondly, the inherent strain values for different partitions were obtained by considering the effects of the extraction method, mesh size, equivalent value layer, and partition size on the inherent strain values. Then, using the inherent strain method, the deformation of large-scale components was predicted rapidly. Comparing the simulation results with the experimental results, the following conclusions were obtained. The deformation predicted by the method proposed in this manuscript is consistent with the deformations predicted using the traditional thermoelastic–plastic method and the experimental method. Significantly, applying the method proposed in this manuscript to predict the deformation of LDM metal components, computational efficiency is improved by 27.25 times compared with results using the conventional thermoelastic–plastic method.

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Section: Introductionmentioning

confidence: 99%

Research on the Deformation Prediction Method for the Laser Deposition Manufacturing of Metal Components Based on Feature Partitioning and the Inherent Strain Method

Li,

Gao,

Yin

et al. 2024

Mathematics

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Application of Linear Mixed-Effects Model, Principal Component Analysis, and Clustering to Direct Energy Deposition Fabricated Parts Using FEM Simulation Data

Pazireh,

Mirazimzadeh,

Urbanic

2024

Materials

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The purpose of this study is to investigate the effects of toolpath patterns, geometry types, and layering effects on the mechanical properties of parts manufactured by direct energy deposition (DED) additive manufacturing using data analysis and machine learning methods. A total of twelve case studies were conducted, involving four distinct geometries, each paired with three different toolpath patterns based on finite element method (FEM) simulations. These simulations focused on residual stresses, strains, and maximum principal stresses at various nodes. A comprehensive analysis was performed using a linear mixed-effects (LME) model, principal component analysis (PCA), and self-organizing map (SOM) clustering. The LME model quantified the contributions of geometry, toolpath, and layer number to mechanical properties, while PCA identified key variables with high variance. SOM clustering was used to classify the data, revealing patterns related to stress and strain distributions across different geometries and toolpaths. In conclusion, LME, PCA, and SOM offer valuable insights into the final mechanical properties of DED-fabricated parts.

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Investigation of residual stresses of multi-layer multi-track components built by directed energy deposition: experimental, numerical, and time-series machine-learning studies

Cited by 2 publications

References 30 publications

Research on the Deformation Prediction Method for the Laser Deposition Manufacturing of Metal Components Based on Feature Partitioning and the Inherent Strain Method

Research on the Deformation Prediction Method for the Laser Deposition Manufacturing of Metal Components Based on Feature Partitioning and the Inherent Strain Method

Application of Linear Mixed-Effects Model, Principal Component Analysis, and Clustering to Direct Energy Deposition Fabricated Parts Using FEM Simulation Data

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